A Study on Applying an Autoregressive Model with the Kalman Filtering in Accuracy Improvement of Dissolved Oxygen Measurement

The dissolved oxygen (DO) measurement plays a crucial role in every automatic aerator-control system for shrimp farming because the DO affects both the animal survival rate and the growth rate. As a consequence, the accuracy degree of the DO sensor matters. The sensor with high accuracy is not economical. Therefore, we propose a framework for accuracy improvement in the DO measurement of the low-cost sensor. The proposed framework is based on the Kalman filtering algorithm in which an autoregressive technique is used to model the state transition. Therefore, it does not require a complex DO dynamic model. Experimental results show that the framework can be used to improve measurement accuracy in most cases. In the best case, our proposed method increases the accuracy by 13.5%. However, the degree of improvement is on a small scale.